Brain connectivity
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Brain resting-state functional connectivity (rsFC), white matter (WM) integrity, and cortical morphometry, as well as neuropsychological performance, have seldom been studied together to differentiate Alzheimer's disease (AD), mild cognitive impairment (MCI), and elderly cognitively healthy comparison (eCHC) samples in the context of the same study. We examined brain rsFC in samples of patients with mild AD (n = 50) and MCI (n = 49) in comparison with eCHC samples (n = 48) and then explored whether rsFC abnormalities can be linked to underlying gray matter (GM) volumetric and/or WM microstructural abnormalities. The mild AD sample showed significantly increased rsFC in the executive control network (ECN) and dorsal attention network (DAN) compared with the eCHC sample, and increased rsFC in ECN compared with MCI. ⋯ Significant GM volumetric reductions were observed in brain regions corresponding to both RSNs in the mild AD sample compared with MCI as well as eCHC samples. The association of default mode network-DAN anticorrelation with cognitive performances differentiated mild AD and MCI from eCHC sample. These findings highlight the association between brain structural and functional abnormalities as well as cognitive impairment that enables differentiation between early AD, MCI, and eCHC samples.
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The goal of this study was to demonstrate that a novel resting state BOLD ALFF (amplitude of low frequency fluctuations)-based correction method can substantially enhance the detectability of motor task activation in the presence of tumor-induced neurovascular uncoupling (NVU). Twelve de novo brain tumor patients who underwent comprehensive clinical BOLD fMRI exams including task fMRI and resting state fMRI (rsfMRI) were evaluated. Each patient displayed decreased/absent task fMRI activation in the ipsilesional primary motor cortex in the absence of corresponding motor deficit or suboptimal task performance, consistent with NVU. ⋯ A novel ALFF-based correction method was used to identify the NVU affected voxels in the ipsilesional primary motor cortex (PMC), and a correction factor was applied to normalize the baseline Z-scores for these voxels. In all cases, substantially greater activation was seen on post-ALFF correction motor activation maps within the ipsilesional precentral gyri than in the pre-ALFF correction activation maps. We have demonstrated the feasibility of a new resting state ALFF-based technique for effective correction of brain tumor-related NVU in the primary motor cortex.
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Numerous studies have identified several large-scale networks within the brain of healthy individuals, some of which have been attributed to ongoing mental activity during the wakeful resting state. While engaged during specific resting-state functional magnetic resonance imaging (fMRI) paradigms, it remains unclear as to whether traditional block-design simple movement fMRI experiments significantly influence these mode networks or other areas. ⋯ Overall, performance of simple self-directed motor tasks does little to change the resting-state functional connectivity across the brain, especially in nonmotor areas. This would suggest that previously acquired fMRI studies incorporating simple block-design motor tasks could be mined retrospectively for assessment of the resting-state connectivity.
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Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). ⋯ After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.
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Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. ⋯ In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.